# FluidStack 依據 OpenAITools.app 於 2026-11-11 查驗的觀測資料，FluidStack（fluidstack.io）是收錄於 AI 開發者工具的 AI 平台，每月產生 15.46萬次造訪，月流量變化量為 +1.05萬（+7.3%）。其主要地理市場為美國（流量佔比 95.7%），並採用付費、需聯絡報價定價模式。依每月流量計算的主要競爭替代方案包括 RunPod（每月 288.1萬次造訪），Nebius（每月 114.57萬次造訪），Vast.ai（每月 125.76萬次造訪）。 FluidStack builds and operates large-scale compute infrastructure for AI, including gigawatt-class data centers and GPU clusters used for training and inference. The company designs its own power, cooling and data center systems, onshores manufacturing and deploys solar generation to supply the power its clusters need. Rather than a self-serve cloud console, FluidStack works with AI labs and enterprises to provision large reserved clusters, with orchestration tooling for the ML frameworks and schedulers those teams already use. ## 主要觀測資料與規格 | 指標 | 數值 | | :--- | :--- | | 工具名稱 | FluidStack | | 代稱 | fluidstack | | Status | active | | Origin | official | | Maintenance | active | | 官方網域 | fluidstack.io | | 網站網址 | https://fluidstack.io | | 分類 | AI 開發者工具, AI 模型, Large Language Models (LLMs) | | 定價模式 | 付費、需聯絡報價 | | 每月造訪次數 | 154,620 | | 月流量變化 | 10,485 | | 月成長率 | +7.3% | | 主要市場 | 美國 | | 市佔率 | 95.7% | | 直接造訪 | 47,869 | | 自然搜尋造訪 | — | | 社群造訪 | — | | 推薦流量造訪 | 13,036 | | 電子郵件造訪 | 5,170 | | 評論分數 | 4.4 | | 收藏數 | 1 | | 支援平台 | 網站 | | 帳務平台 | stripe | | 屬性 | 功能: 網站; 價格: 付費; 價格: 需聯絡報價 | | 標籤 | AI 雲端, GPU 雲端, NVIDIA GPU, AI 培訓, AI 推理, Kubernetes, Slurm, H100, A100, H200, GB200, 機器學習, 深度學習 | ## 總覽 FluidStack builds and operates large-scale compute infrastructure for AI, including gigawatt-class data centers and GPU clusters used for training and inference. The company designs its own power, cooling and data center systems, onshores manufacturing and deploys solar generation to supply the power its clusters need. Rather than a self-serve cloud console, FluidStack works with AI labs and enterprises to provision large reserved clusters, with orchestration tooling for the ML frameworks and schedulers those teams already use. ## 價格與方案 Enterprise and contact-for-pricing. FluidStack does not publish per-GPU-hour rates or a self-serve sign-up on its website; capacity is scoped and quoted through a direct sales conversation based on cluster size, duration and hardware. Catalogue listings classify it as a paid product requiring contact for pricing. ## 關鍵功能與能力 - **Datacenter-scale compute built in-house:** FluidStack designs and builds large AI data centers, including high-density campuses with significant per-site power capacity, rather than reselling capacity from other clouds. - **GPU clusters for training and inference:** Clusters are architected for large-scale model training and inference, with high-bandwidth interconnect and orchestration suitable for distributed workloads. - **Power and cooling engineered together:** The company develops off-grid solar and recirculating closed-loop cooling, treating power and water efficiency as core engineering problems. - **Managed operations and reliability:** Smart hands, cluster provisioning and operations support let research teams focus on models while FluidStack runs the physical and orchestration layers. - **US-built and onshored supply chain:** Data centers are built in the United States with onshored manufacturing of compute and power infrastructure, a deliberate supply-chain and sovereignty choice. ## 主要使用情境 - **Train large models at scale (ML platform engineer):** Reserve large, dedicated GPU clusters for multi-week or multi-month runs where network topology and uninterrupted availability dominate the decision. - **Serve models in production (Infrastructure lead):** Stand up capacity for serving models in production with managed operations, instead of assembling colocation, power and cooling yourself. - **Plan power-constrained expansion (AI lab operations):** Work with a partner that can design power generation and cooling alongside compute, so energy availability does not become the bottleneck to scaling. - **Meet sovereignty and location requirements (Enterprise technology strategist):** Evaluate a dedicated, US-located AI data center option for workloads with data-residency or sovereignty requirements. ## 優勢 - Purpose-built AI infrastructure rather than generic cloud resale - Power, cooling and data center design handled in-house, which matters at scale - Managed provisioning and operations for large training and inference clusters - Explicit positioning on US-built, onshored infrastructure ## 限制 - No published self-serve pricing or instant sign-up; access requires a sales conversation - Enterprise-scale focus means very small individual workloads may not be a fit - Capacity is concentrated in US locations, which may matter for latency-sensitive or data-residency-bound teams ## 常見問題 ### How much does FluidStack cost? FluidStack's website does not publish self-serve instance pricing. Access is arranged through a sales engagement, and the tools directory lists it as a paid, contact-for-pricing product. Teams with large training or inference requirements should reach out directly for a quote. ### Who is FluidStack designed for? It is aimed at AI labs, model developers and enterprises that need large blocks of GPU capacity for training and inference, from single clusters up to very large multi-site deployments. ### What hardware does FluidStack provide? The company describes building GPU clusters with modern NVIDIA accelerators for AI training and inference, and its public materials emphasise designing compute, power and cooling from first principles at very large scale. ### Where is FluidStack's infrastructure located? Its clusters are built and operated in the United States, with supporting manufacturing onshored and off-grid solar being developed to power compute as it scales. ### FluidStack 的每月流量與成長趨勢為何？ FluidStack 目前每月有 15.46萬次造訪，月流量變化為 +1.05萬（成長率 +7.3%）。其主要來源市場為美國（佔比 95.7%），其中包含每月 4.79萬次直接造訪與每月 — 次自然搜尋造訪。 ### FluidStack 是免費的嗎？其定價模式為何？ FluidStack 採用付費、需聯絡報價定價模式。Enterprise and contact-for-pricing. FluidStack does not publish per-GPU-hour rates or a self-serve sign-up on its website; capacity is scoped and quoted through a direct sales conversation based on cluster size, duration and hardware. Catalogue listings classify it as a paid product requiring contact for pricing. 支援的帳務與結帳平台包括：stripe。 ### 2026 年最佳的 FluidStack 替代方案有哪些？ 依據 OpenAITools.app 的分類流量與功能契合度，FluidStack 的前五大替代方案為：1. RunPod（每月 288.1萬次造訪）; 2. Nebius（每月 114.57萬次造訪）; 3. Vast.ai（每月 125.76萬次造訪）; 4. Together AI（每月 164.59萬次造訪）; 5. Modal（每月 119.6萬次造訪）。 ### FluidStack 支援哪些平台與核心 AI 能力？ FluidStack 分類於 AI 開發者工具, AI 模型, Large Language Models (LLMs)，支援以下平台：網站，其核心能力標籤包括：AI 雲端, GPU 雲端, NVIDIA GPU, AI 培訓, AI 推理, Kubernetes。 ## 替代方案 | 工具名稱 | 每月造訪次數 | 定價模式 | 比較 | | :--- | :--- | :--- | :--- | | [RunPod](https://openaitools.app/tw/tool/runpod) | 2,881,043 | 付費 | [比較](https://openaitools.app/tw/compare/fluidstack-vs-runpod) | | [Nebius](https://openaitools.app/tw/tool/nebius) | 1,145,711 | 付費、需聯絡報價 | [比較](https://openaitools.app/tw/compare/fluidstack-vs-nebius) | | [Vast.ai](https://openaitools.app/tw/tool/vast-ai) | 1,257,554 | 付費 | [比較](https://openaitools.app/tw/compare/fluidstack-vs-vast-ai) | | [Together AI](https://openaitools.app/tw/tool/together-ai) | 1,645,930 | 免費增值、付費 | [比較](https://openaitools.app/tw/compare/fluidstack-vs-together-ai) | | [Modal](https://openaitools.app/tw/tool/modal-com) | 1,196,021 | 免費增值、需聯絡報價 | [比較](https://openaitools.app/tw/compare/fluidstack-vs-modal-com) | | [Cerebras](https://openaitools.app/tw/tool/cerebras) | 701,013 | 需聯絡報價 | [比較](https://openaitools.app/tw/compare/cerebras-vs-fluidstack) | | [DeepInfra](https://openaitools.app/tw/tool/deep-infra) | 457,810 | 付費 | [比較](https://openaitools.app/tw/compare/deep-infra-vs-fluidstack) | | [fal.ai](https://openaitools.app/tw/tool/fal-ai) | 3,093,684 | Paid, pay-as-you-go usage-based pricing | [比較](https://openaitools.app/tw/compare/fal-ai-vs-fluidstack) | | [Cirrascale](https://openaitools.app/tw/tool/cirrascale-com) | 27,190 | 付費 | [比較](https://openaitools.app/tw/compare/cirrascale-com-vs-fluidstack) | | [TensorDock](https://openaitools.app/tw/tool/tensordock) | 19,689 | 付費 | [比較](https://openaitools.app/tw/compare/fluidstack-vs-tensordock) | | [Salad](https://openaitools.app/tw/tool/salad-gpu-cloud) | 713,519 | 免費試用、付費、需聯絡報價 | [比較](https://openaitools.app/tw/compare/fluidstack-vs-salad-gpu-cloud) | | [Anyscale](https://openaitools.app/tw/tool/anyscale-com) | 49,779 | 付費、需聯絡報價 | [比較](https://openaitools.app/tw/compare/anyscale-com-vs-fluidstack) | --- 來源：https://openaitools.app/tw/tool/fluidstack